The Impact of Product Portfolio Strategy on Financial Performance: The Roles of Product Development and Market Entry Decisions

The Impact of Product Portfolio Strategy on FinancialPerformance: The Roles of Product Development and MarketEntry DecisionsWooseong Kang and Mitzi Montoya

Innovation is one of the most important issues facing business today. The major difficulty in managing innovation is thatmanagers must do so against a constantly shifting backdrop as technologies, competitors, and markets constantlyevolve. Managers determine the product portfolio through key decisions about product development and market entry.Key strategic questions are what portfolio strategies provide the greatest reward. The purpose of this study is tounderstand the relative financial values of each component of a product portfolio. Specifically, the paper examines theshort-term and long-term financial impacts of product development strategy and market entry strategy. These strategiesreflect two critical tensions that must be balanced in product portfolio decision making and essentially determine afirm’s product portfolio. In doing so, the paper also investigates how a firm’s capabilities drive each component of aproduct portfolio.

From the empirical analyses in the context of the biomedical device industry, the paper found important insightsregarding product portfolio strategies. First, a large product portfolio helps a firm’s financial performance. Inparticular, the pioneering new products have strongest impacts on short-term performances, and nonpioneeringmature products do not provide significant contribution. Second, the results indicate a persistent first-mover advan-tage. The first-to-market new products yield not only an immediate effect, but also persistent long-term effects,suggesting that it is important to be first in the market even though there may be short-term losses. Third, the resultssuggest the need to balance between “mature” and “new” products. Also, firms need to balance “first-to-market”and “late-entered” products. Because a new or pioneering product requires more resource, it may hurt other prod-ucts in the portfolio. Thus, without support from mature or follower products, new products and pioneering productsalone may not increase firm sales or profit. Fourth, from a long-term perspective, the paper found that the financialmarket only rewards a firm’s overall capability to deliver new products first in the marketplace. Thus, short-termperformance is mainly driven by product-level innovativeness, whereas firm-level innovativeness enhances forward-looking long-term performance. Fifth, the paper also found that pioneering new products are driven by integratingboth primary and complementary technological capabilities. And nonpioneering new products are mainly driven bythe capabilities in primary technology domain. These results provide important insight into the relative value andtiming of return on investment in radical versus incremental innovation and alternative market entry strategies. Byunderstanding the performance trade-offs of these different factors in the short and long term, one can developbetter guidelines for optimizing innovation strategies, and their dependence on both external and internal environ-mental conditions.

Introduction

B y finding new solutions to problems, innovationhas the potential to create new markets andtransform industries—or completely destroy

existing ones. Innovation, the process of bringing newproducts and services to market, is generally perceived to

be fundamental to firm growth and survival as well asnational economic prosperity (Cefis and Marsili, 2006;Furman, Porter, and Stern, 2002; Hauser, Tellis, andGriffin, 2006). In fact, successfully innovating firms con-sistently outperform competitors (O’Connor, Hyland, andRice, 2004).

The literature on innovation has been concerned withhow well an innovative firm can generate the economicvalue from the innovations (Cockburn and Griliches,1988; Harabi, 1995; Teece, 1986; Winter, 2006). Asresearch continues to show positive and increasing

Address correspondence to: Wooseong Kang, Department ofManagement, Dongguk University-Seoul, Seoul 100-715, Korea. E-mail:[email protected]. Tel: +82-2-2260-3237.

J PROD INNOV MANAG 2014;31(3):516–534© 2013 Product Development & Management AssociationDOI: 10.1111/jpim.12111

mailto:[email protected]

returns for innovation investments (e.g., Bayus, Erickson,and Jacobson, 2003; Chandy and Tellis, 2000; Sorescu,Chandy, and Prabhu, 2003) and global markets becomeincreasingly competitive, innovation has become anexplicit goal for many firms. Many of these innovationgoals have been translated into product portfolio strate-gies characterized by emphasis on higher rates of newproduct introduction, radical innovation, and/or speed tomarket. For example, some firms have set targets forproduct portfolio “freshness” as defined by the percent-age of current year sales derived from products recentlydeveloped (e.g., Proctor & Gamble and 3M). This sort oftarget is driving higher rates of new product introduction.Other firms have set targets for being market share ortechnological leaders, which is driving both incrementalinnovation as well as efforts to be first-to-market withhighly innovative new products (e.g., GE and Intel). Insupport of this innovation activity is an ever-growingservice sector of consultants who promote strategies andtools for increasing the rate of new product introduction,discovering radical innovations, and reducing time tomarket (e.g., IBM and IDEO).

While there is general acceptance that innovation is avaluable activity (e.g., Hauser et al., 2006), prior researchon the value of innovation has not fully examined thefinancial impacts of different product portfolio strategies.As a result, managerial understanding of the financialimpact of different innovation strategies is incomplete.Success in innovation is achieved by understanding thecomponents of innovation strategy that determine theproduct portfolio, the relative value of each component,and managing both so that little gets left to chance. Thereis a pressing need to connect product portfolio innovationstrategies with financial metrics (Hauser et al., 2006). Inaddition, it is also important to note that innovations

require a firm to draw on existing competencies(Danneels, 2004; Helfat and Peteraf, 2003). In order tofully understand the value of innovation, an integratedperspective of connecting firm capabilities, innovation,and financial performance is much needed.

Thus, the purpose of this study is to understand therelative financial values of each component of a productportfolio as defined by innovation strategy. Specifically,the paper first examines the short-term and long-termfinancial impacts of two core components of a firm’sinnovation strategy: product development strategy andmarket entry strategy. These strategies reflect twocritical tensions that must be balanced in productportfolio decision making and essentially determine afirm’s product portfolio. From a product portfolio per-spective, product development and market entry strate-gies are characterized by new product introductions andmarket entry timing, respectively. Then, the paper inves-tigates how a firm’s capabilities drive each componentof a product portfolio, which require different sets ofcapabilities.

While prior research has examined elements ofproduct development and market entry strategies sepa-rately (e.g., Bayus et al., 2003; Chaney, Devinney, andWiner, 1991; Golder and Tellis, 1993; Sorescu et al.,2003; Wind and Mahajan, 1997), the paper investigatesboth components simultaneously from a product portfo-lio management perspective. By doing so, the effects of aparticular strategy can be isolated by controlling otherstrategies. In addition, the paper further investigates thepotential interactions between these two strategies. Indoing so, it is important to examine the entire productportfolio (instead of new products only) and multiplefinancial outcomes (instead of short-term outcomes only)because an individual product has multiple contributionsto the firm’s long-term financial performance.

The context of the study is the biomedical deviceindustry using 15 years of product portfolio and financialdata. The biomedical device industry provides a veryfitting context to investigate innovation. In this industry,innovation is a key driver of the growth, and there issignificant variability in innovation strategies acrossfirms. Thus, it can be easily generalizable to many otherdynamic, globally competitive industries.

Literature

Technological Innovations

The technological innovation comprises two basic ideas:(1) the innovation process consists of the technological

BIOGRAPHICAL SKETCHES

Dr. Wooseong Kang is an associate professor of marketing at DonggukUniversity-Seoul, South Korea. Prior to joining the marketing faculty atDongguk University, he worked at North Carolina State University andearned his Ph.D. in marketing from the University of North Carolina atChapel Hill. Dr. Kang’s teaching and research interests are in the areasof new product and innovation management, product systems market,and multimarket competition issues.

Dr. Mitzi Montoya (Ph.D. marketing and statistics, B.S. general engi-neering, Michigan State University) is the executive dean of the Collegeof Technology and Innovation at Arizona State University. Dr. Mon-toya’s research interests lie at the intersection of technology, marketing,and virtual team dynamics. Her research focuses on innovation pro-cesses and strategies and the role of technology as an enabler of collab-orative, distributed team decision making.

PRODUCT PORTFOLIO AND FINANCIAL PERFORMANCE J PROD INNOV MANAG 5172014;31(3):516–534

development and the market introduction of an inven-tion, and (2) the innovation process is iterative, includ-ing the first introduction of a new innovation and thereintroduction of improved ones (Garcia and Calantone,2002; OECD, 1991). An invention becomes an innova-tion only after it is actually produced and marketed intothe marketplace.

From this perspective, Dosi (1982) proposed two basicapproaches to prime movers of inventive activity:“demand pull” (i.e., market forces as the main determi-nants) and “technology push” (i.e., technology as anautonomous factor). The framework accounts for bothcontinuous changes, along a technological trajectorydefined by a technological paradigm, and discontinuitiesin technological innovation, which are associated withthe emergence of a new paradigm. The new technologicalparadigm stems mainly from the interplay between tech-nology and market factors.

Abernathy and Clark (1985) also focus on competitivesignificance of technology competence and market envi-ronment, and proposed four categories for innovations:“architectural,” “niche creation,” “regular,” and “revolu-tionary.” Architectural innovations create a new market/industry with new technology, and they establish thefuture architecture of the industry. In niche creation,existing technical competences are refined, and productapplicability is improved in new market segments.Regular innovations build on established technicalcompetences and existing markets, including incrementalprocess improvements. Revolutionary innovationsdisrupt the established technical competence, but targetexisting markets. Thus, it is necessary to consider bothtechnology and market dimensions to understand productinnovations. Typically, these two dimensions are embed-ded in a firm’s product development and market entrystrategies. From an extensive review on the varying per-spectives for innovativeness, Garcia and Calantone(2002) confirmed that product innovation is indeed mea-sured by the potential discontinuity in the marketing andtechnological process.

It is also important to note that innovations do notoccur just during the product development stage, but alsomay undergo continual improvements during the diffu-sion process (Rogers, 1995). Utterback and Abernathy(1975) also pointed out the iterative nature of productinnovation, resulting in different innovation types:“radical innovations” for products at the early stages and“incremental innovations” at the later stages of theproduct life cycles. Initially, products will be emphasizedon performance, then on variety, and later on productstandardization and costs.

Technological Capabilities to Innovation

According to the resource-based view (RBV), capabili-ties are defined as “complex bundles of skills and accu-mulated knowledge that enable firms to coordinateactivities and make use of their assets” (Day, 1990), andcapabilities are seen as the way by which firms deploytheir resources in order to achieve a desirable objective,such as sustainable competitive advantage (Black andBoal, 1994; Dutta, Narasimhan, and Rajiv, 2005).

Innovations require a firm to draw on existing compe-tencies (Danneels, 2004; Helfat and Peteraf, 2003). Suc-cessful product innovation typically requires strengthsin both marketing and technology. Because of the“technology-push” nature of most radical innovation,technology-related capabilities are relatively more impor-tant than market-related capabilities in radical innovation(O’Connor, 1998). Often the most important role of mar-keting in radical innovation is to identify possiblemarkets (Abernathy and Utterback, 1978). Di Benedetto,DeSarbo, and Song (2008) empirically examined strate-gic capabilities (i.e., marketing, market linking, technol-ogy, information technology, and management related)as drivers of the radical innovations and found thattechnology-related capabilities are indeed positivelyrelated to radical innovations.

Especially for continuous innovations, the synergiesbetween new and existing technological capabilities arecritical. Technological knowledge similarities facilitatethe exchange and combination of existing knowledge(Nonaka, Takeuchi, and Umemoto, 1996) and encourageexploitation of existing competence. And the new techno-logical knowledge can be more easily understood, assimi-lated, and applied (Cohen and Levinthal, 1990; Lane andLubatkin, 1998). Thus, the firm with homogenous knowl-edge in a single primary domain is more likely to producehigh-quality inventions in the similar technology domain(Fleming, 2001). At the same time, firms with knowledgesimilarity search for new solutions in the “neighborhood”of existing domains. This reduces the opportunities forradically new knowledge and deters exploratory learning.Thus, knowledge similarities are less likely to produceradical innovations (Fleming, 2001).

On the other hand, technological knowledgecomplementarities facilitate the exploration processthrough experimentation with new competencies (March,1991). The complementary knowledge helps extend thescope of invention search, thereby contributing to richerinventions and innovations (Katila and Ahuja, 2002). Theintegration of these complementary knowledge and exist-ing primary knowledge stocks can facilitate a much

518 J PROD INNOV MANAG W. KANG AND M. MONTOYA2014;31(3):516–534

greater portfolio of new and unique knowledge combina-tions and innovations (Makri, Hitt, and Lane, 2010).

The Value of Innovation

Various explanations have been offered as to why inno-vation creates value for the firm. According toSchoonhoven, Eisenhardt, and Lyman (1990), innovationis a central mechanism whereby organizations diversify,adapt, and reinvent themselves in response to changingmarket and technical conditions. Wang and Chen (2010)found that greater value appropriation from innovations ispositively associated with firm specificity of its innova-tion knowledge by capturing a larger share of the valuegenerated from its innovations. A review of the literatureon the link between innovation and competitive advan-tage suggests four central tenets linking innovation to thefirm value (e.g., Lengnick-Hall, 1992). First, innovationsthat are hard to imitate are more likely to lead to sustain-able competitive advantage (e.g., Clark, 1987; Porter,1985). Inimitability is achieved through strategic choicesthe firm makes with regard to product/market arena,design, information processing, coordination, humanresource management, and other strategic resources.Second, innovations that accurately reflect market reali-ties are more likely to lead to sustainable competitiveadvantage (e.g., Porter, 1985). These innovations includeproducts with desired attributes and high perceivedutility, resulting in reasonable market size and sufficientreturn. Third, innovations that enable a firm to exploit thetiming characteristics of the relevant industry are morelikely to lead to sustainable competitive advantage (e.g.,Betz, 1987; Kanter, 1983). That is, the timing can sub-stantially influence the cost and return of innovationactivities (e.g., Hambrick, 1982). Fourth, innovations thatrely on capabilities and technologies that are readilyaccessible to the firm are more likely to lead to sustain-able competitive advantage (e.g., Ansoff, 1988; Miller,1990). Contingency perspectives suggest that differenttypes of innovations require different types of competen-cies, and a firm’s ability to effectively leverage theright (and available) resources determines competitiveadvantage.

The value of firm-specific innovations may be erodedbecause of environmental dynamics, including a misfitbetween the innovations and changing environments anda low adaptability to the environment (Freeman andHannan, 1983; Ghemawat and Del-Sol, 1998). In order toaddress this risk, the firm may apply its innovation tobroader technological domains (Wang and Chen, 2010).Because environmental changes are unlikely to affect

multiple technological domains at the same time, thetechnological diversity allows a firm to better avoid therisk of firm-specific innovations (Markowitz, 1959). Inaddition, by enhancing the firm’s abilities to recombinediverse knowledge bases and increasing the number ofalternatives, technological diversity could expand a firm’sadaptability to the environmental changes (Fleming,2001; Fleming and Sorenson, 2001). Thus, firms withdiverse technological competences can mitigate the envi-ronmental risks on the relationship between firm-specificinnovation and the value appropriation.

In concert, these discussions regarding the relation-ships among capability, innovation, and competitiveadvantage suggest that the selection of competitivestrategy determines the value of innovation. From apractical perspective, competitive strategy is determinedby product portfolio decision. In fact, choosing theproduct portfolio determines the firm’s strategy for themedium-term future and is senior management respon-sibility (Cooper, Edgett, and Kleinschmidt, 2001;Roussel, Saad, and Erickson, 1991). Thus, deeper under-standing of how the components of product portfolio,characterized by product development strategy andmarket entry strategy, contribute to competitive advan-tage and firm performance is much needed. Past researchsuggests that better-managed firms structure their port-folios by striking a balance in the product portfolioacross these strategic components (Bayus et al., 2003;Chaney et al., 1991; Golder and Tellis, 1993; Sorescuet al., 2003; Wind and Mahajan, 1997). However, pastresearch has not systematically decomposed the compo-nents of product portfolio strategy to examine how eachcomponent is driven by technological capabilities anddiffers in terms of impacts on short- and long-termfinancial performance. The conceptual framework inte-grating these discussions is shown in Figure 1. In thefollowing sections, the two strategic components ofproduct portfolio strategy in relation to firm performanceare further discussed.

Product Development Strategy: New ProductIntroduction and Introduction Intensity

It is necessary to note that product innovation is notalways equal to firm innovativeness (Garcia andCalantone, 2002). Firm innovativeness is defined as afirm’s propensity to innovate or develop new products(Ettlie, Bridges, and O’Keefe, 1984) and as thepropensity to adopt innovation (Damanpour, 1991;Rogers, 1995). That is, product innovativeness andfirm innovativeness could be distinct constructs, and


highly innovative products does not always imply highfirm innovativeness (Garcia and Calantone, 2002). Thus,the product innovativeness is a “realization” of firminnovativeness. Several well-established managementframeworks—including the product life-cycle conceptand the growth–share matrix—suggest that firms need tocontinuously introduce new products to maintain marketvalue and ensure firm survival (Ansoff, 1988; Day, 1981).Theoretically, the RBV of the firm also suggests thatongoing innovation contributes to dynamic capabilitiesthat are particularly critical to achieving or maintainingcompetitive advantage in rapidly changing market envi-ronments (e.g., Barney, 1991; Porter, 1985; Teece,Pisano, and Shuen, 1997). Operationally, the productinnovativeness of product development strategy is char-acterized by the actual number of new products at a givenpoint in time, and the firm innovativeness is reflected inthe relative intensity (or rate) of new product introductionover time, i.e., introduction intensity.

Much prior research on the value of innovation hasused research and development (R&D) spending orpatent activity as indicators of innovation activities (cf.Griliches, 1998; Klette and Kortum, 2004; Pakes, 1985).While these are important inputs to the innovationprocess, at best they are indirectly linked to innovationoutcomes reflected in managerially controllable productportfolio decisions. Ultimately, managers must makedecisions regarding product development strategy bylinking user needs to specific product development proj-ects and making operational commitments. A select fewstudies have empirically linked new product introductionactivity to financial performance (Bayus et al., 2003;Chaney et al., 1991; Pauwels, Silva-Risso, Srinivasan,

and Hanssens, 2004). Although the results are mixed, thegeneral finding is that new product introduction is posi-tively related to financial performance. However, it is alsoimportant to examine the introduction intensity by utiliz-ing the entire product portfolio in order to understand thefirm-level innovation capability and the effects of newproducts on the other products in the portfolio. One newproduct alone may enhance firm performance. In addi-tion, if the effects on the other products are complemen-tary, the total effect on the portfolio will be amplified.However, because of resource constraints or cannibaliza-tion (e.g., Chandy and Tellis, 1998), introducing newproducts may hurt the performance of other products,leading to negative indirect effect.

In setting the product development strategy in termsof new product introductions and the introduction inten-sity, firms consider two opposing incentives. Accordingto Bordley (2003), highly diverse product lines helpfirms to better satisfy heterogeneous needs and wants(Connor, 1981; Lancaster, 1979; Quelch and Kenny,1994), where product diversity is achieved through abroad range of product offerings that are regularly“refreshed” through continuous innovation and newadditions. Similarly, a diverse product line may deternew entering competitors (Bananno, 1987; Brander andEaton, 1984; Schmalensee, 1978) and lead to higherprices among remaining firms (Benson, 1990; Putsis,1997). This perspective suggests that the number of newproducts and the introduction intensity are positivelyrelated to financial performance. On the other hand,another stream of research suggests that a narrowerproduct line enables the firm to lower production costsbecause of scale economies (Baumol, Panzar, andWillig, 1982), where narrowness is achieved through afocused product portfolio with fewer distinct productofferings. According to this perspective, less change inthe product portfolio can lower design, production, andinventory holding costs, and reduce complexity inassembly (Lancaster, 1979, 1990; Moorthy, 1984). Thisview suggests that the excessive number of new productsand the introduction intensity may be negatively relatedto financial performance.

These two perspectives represent demand-side andsupply-side countervailing forces that affect the financialconsequences of product development strategies. Whilethe limited prior empirical research to date suggests anoverall positive relationship, the relationship betweenproduct development strategy and firm performance isnot necessarily a linear one. The research goal is to dis-aggregate the short- and long-term financial implicationsof product development strategies.

Firm Performance: • Short-term Performance

– Sales – Profit

• Long-term Performance – Stock Return

Controls: • Firm Level −Past Performance −Firm Size

• Market Level −Market Diversity

• Competition Level −Competing Products

Portfolio Strategy: • Product Development Strategy

- Introduction Intensity • Market Entry Strategy

- Pioneering Intensity • Portfolio Components

- Leading-Mature - Following-Mature - Leading-New - Following-New

Figure 1. Conceptual Framework


Market Entry Strategy: First-to-Market andPioneering Intensity

At an industry level (i.e., macro-level perspective),“innovativeness” is the capacity of a new innovation tocreate discontinuities in technology and market structureof an industry. This perspective is concerned with mea-suring how the product innovation is new to the market oran industry (Atuahene-Gima, 1995; Lee and Na, 1994;Mishra, Kim, and Lee, 1996). On the other hand, at a firmlevel (i.e., micro perspective), “innovativeness” is thecapacity of a new innovation to affect the firm’s existingmarketing and technological resources. From this per-spective, product innovativeness can be identified as newto the firm or the customers (More, 1982).

Market entry strategy is concerned with the timing ofnew product introductions. Similar to the product devel-opment strategy, from a product portfolio perspective,product-level market entry strategy is characterized bythe number of first-to-market products in the portfolio,and the firm-level market entry strategy is reflected in theintensity (or rate) of first-to-market products introducedover time, that is, pioneering intensity. These two char-acteristics of market entry strategy describe severalmuch-debated aspects of product introduction timing.First, it is not straightforward to define or measure thedegree of innovativeness. Reviews of the literature on thedefinition of innovativeness (Chandy and Tellis, 1998;Garcia and Calantone, 2002) suggest a multitude of con-ceptualizations and measurement approaches. Definitionsgenerally focus on technology newness, the degree ofcustomer need fulfillment, and/or market newness with awide range of measurement approaches including objec-tive and subjective data. In this paper, we take a conser-vative approach and define innovativeness objectively interms of newness to the firm and newness to the market.Products that are new to the market (if successful) tend tocreate new markets. Usually, first-to-market productsbring new technology, new features, and new marketingstrategies that appeal to the customers. Thus, first-to-market products tend to be more innovative and more(versus less) innovative products tend to require distinctcapabilities to achieve market success.

Second, past research has examined market entrytiming from an internal cost perspective and an externalmarket advantage perspective. That is, the market entrydecision represents an important trade-off of cannibaliza-tion versus faster accrual of profit (Krishnan and Ulrich,2001). This view reflects an internal process-oriented per-spective of innovation and its impact on cost structure(and therefore, profit). The general wisdom of this view is

that faster development cycles reduce the discrepanciesbetween the development and launch periods. Thus, firmperformance is a function of speeding up the develop-ment cycle without deteriorating the quality of theproduct and its price (Cohen, Eliashberg, and Tech-Hua,1996; Wind and Mahajan, 1997). According to this per-spective, the first-to-market launches and the pioneeringintensity are positively related to financial performance.

From a competitive strategy perspective, market entrytiming is a question of first-mover or pioneering advantage(Golder and Tellis, 1993; Kerin, Varadarajan, andPeterson, 1992; Wind and Mahajan, 1997). This stream ofresearch suggests that the benefits of early entry includenot only increased profitability, but also advantages relatedto market share and customer mind share. The challengeassociated with the first-mover or pioneering perspectiveis balancing the degree to which the market is primed andready for the innovation while taking into considerationthe capabilities of the firm to provide the necessarysupport. Past research suggests that the financial rewardsfor investment in more (versus less) innovative productsvaries but is generally positive in the long-term (Pauwelset al., 2004). Sorescu et al. (2003) found that the financialreward is the same in the short and long term, but there isvariance related to firms’ resource base, particularly interms of the per-product marketing and technologysupport. These results suggests there should be somebalance in market entry strategy to account for multipletime horizons in financial performance (shorter and longerterm considerations) and to increase organizational capa-bility for learning (Wind and Mahajan, 1997). While theprior empirical research to date suggests an overall posi-tive relationship, the relationship between market entrystrategy and firm performance is not necessarily a linearone. Also, it is not clear that any new product has the samepersistent long-term effect, regardless of market entrytiming. The research goal is to disaggregate the short- andlong-term financial implications of market entry. In doingso, the paper also investigates the potential interactionsbetween product development and market entry strategies.

Empirical Context: BiomedicalDevice Industry

The United States is the global leader in innovation inmedical technology development and the largest pro-ducer of medical devices and diagnostics (see Figure 2).The biomedical device industry is a growth sector of theeconomy by all measures—employment trends, produc-tion values, global market share, venture capital financ-ing, and R&D expenditures—and it is supported by


growing worldwide demand for health services. Thebiomedical device industry includes large and smalldevices for many application domains, including dental,eletromedical, lab analytical, ophthalmic, orthopedic,prosthetic, imaging supplies, and surgical instrumentsand textiles (Food and Drug Administration [FDA]). Theindustry is generally considered a high-technology sectorthat faces the challenge of converting discoveries andresearch breakthroughs into public acceptance andmarket success. The rate of innovation and technologicalchange in the industry is being driven by emerging break-throughs in a wide range of technological domains suchas device miniaturization, nanotechnology, molecular andgene-based diagnostics, information technology, and arti-ficial organs and tissue engineering. Thus, technologicalcapabilities are important drivers of product innovationsin this industry.

While there is a growing body of research that usespharmaceutical industry data to address a variety of man-agement research questions (e.g., Sorescu et al., 2003),no prior research has used biomedical device industrydata. Products in the biomedical device industry aretreated differently by the FDA and generally get tomarket more quickly than pharmaceutical drugs (genericor branded) and/or biotechnology products. Unlike thepharmaceutical industry that relies on a “blockbuster”and acquisition-based development approach to drug dis-covery, the biomedical device industry has a much moretraditional, balanced customer- and technology-drivenproduct development process.

The study involves the development of an extensivedatabase of innovation activities in the biomedical deviceindustry across 14 years. The data set is mainly built from

three sources. First, a complete census of new productintroduction activities for biomedical devices from theFDA database was collected. The FDA data allow us todevelop proxy measures of product development andmarket entry strategies, as well as indicators of competi-tive intensity within specific product categories duringthe same time period. Second, annual financial informa-tion for all public firms that sold biomedical devices overthe same time period using COMPUSTAT/CRSP datawere collected. The COMPUSTAT/CRSP data provide uswith various indicators of financial performance, as wellas organizational support/assets and marketing capability.Third, annual patent data from the U.S. Patent and Trade-mark Office (USPTO) for the same firms was obtained.The patent data allow us to develop proxy measures of afirm’s technological capability. In total, the data cover 14years of product portfolio activities in the medical deviceindustry (1991–2004), including 138 public firms in 1578product-market segments, with a total of 3471 products.The medical device industry includes seven subindustriesdefined by the COMPUSTAT database and 19 medicalspecialties defined by the FDA. Seven subindustriesare (1) DENTAL EQUIPMENT AND SUPPLIES,(2) ELECTROMEDICAL APPARATUS, (3) LABANALYTICAL INSTRUMENTS, (4) OPHTHALMICGOODS, (5) ORTHO, PROSTH, SURG APPL, SUPLY,(6) SURGICAL, MED INSTR, APPARATUS, and (7)X-RAY AND RELATED APPARATUS. Note that theanalysis excludes the pharmaceutical category to focuson firms whose main business is medical devices. As aresult, several big pharmaceutical firms (e.g., Pfizer andJ&J) are not a part of the data even though they introducesome medical devices in the industry. The medical

0

100

200

300

400

500

600

700

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

New

Pro

duct

s

-40

-20

0

20

40

60

80

Sale

s an

d R

etur

n

New Products Total Sales (Billions) Average Return (%)

Figure 2. U.S. Biomedical Device Industry Statistics


specialties are anesthesiology, clinical chemistry, cardio-vascular, dental, ear, nose, and throat, gastroenterology/urology, hematology, general hospital, immunology,microbiology, neurology, obstetrics and gynecology,ophthalmic, orthopedic, pathology, physical medicine,radiology, general and plastic surgery, and clinicaltoxicology.

It is important to note that the definition of “product”in this context reflects fundamental product developmentstrategies. It is not branded variants that may be part of afirm’s sales strategies. Branded variants do not necessar-ily have to go through the FDA approval process ifcore functionality, manufacturing processes, marketingclaims, and diagnostic applications are not changed.Also, by using market behavior data, the paper focuses onthe financial impact of the realized product portfoliostrategy rather than strategic intentions or subjectiveevaluations. For example, the PIMS project, whichrelates marketing to firm performance, was largely basedon relative or subjective measures, causing comparabilityissues. Ailawadi, Dant, and Grewal (2004) investigatedthe issue, finding mismatch between two types of mea-sures (i.e., the correlations are about .3). That is, it ispossible that subjective measure overstates the strength ofsome relationships (Lehmann and Reibstein, 2006). In

the analysis, instead of relying on subjective or retrospec-tive evaluations of the degree of innovativeness of prod-ucts (e.g., Garcia and Calantone, 2002), the study utilizesthe objective definitions in the FDA data that definenewness of a product to the market.

Model Development: Performance Model

In order to test the conceptual model, the dependent vari-able (i.e., firm performance) and the measurement issuesare described, followed by the empirical model to beestimated, along with the associated estimation issues.Variable definitions and descriptive statistics are reportedin Table 1, and correlations among key variables are inAppendix.

Firm Performance

As noted in the preceding discussions of product devel-opment strategy and market entry strategy, prior researchhas linked select elements of the product portfolio strat-egy to various aspects of firm performance. Overall, pastresearch generally indicates that revenue and profit gen-eration depend on the degree of product innovativenessand the timing of market entry (e.g., Bayus et al., 2003;

Table 1. Variable Definitions and Descriptive Statistics

Variable Definition Mean Standard Deviation

Portfolio componentsMatureProd Number of existing products in portfolio (= Leading-Mature +

Following-Mature)13.91 37.82

Leading-Mature Number of first-entered existing products 8.16 21.73Following-Mature Number of late-entered existing products 5.75 18.38NewProd Number of new products in portfolio (= Leading-New + Following-New) 3.12 10.59Leading-New Number of first-entered new products 1.17 4.53Following-New Number of late-entered new products 1.96 7.89

Portfolio strategyIntroduction intensity Percentage of new products in product portfolio, weighted by time since

introduction.17 .34

Pioneering intensity Percentage of first-entered products in product portfolio (= [number offirst-entered products]/[number of products in portfolio])

.26 .34

ControlSize Assets (in million dollar) 502.05 1461.70CProducts Average number of competing products in product-market segments 2.68 3.07

Firm performanceSales Sales (in million dollar) 405.29 1048.95Cost Cost (in million dollar) 157.06 404.05Profit Sales—cost of selling 248.24 691.61

Technological capabilityPrimary patents Number of cumulative patent applications in medical device industry 38.9 173.7Complementary patents Number of cumulative patent applications in nonmedical device industry 35.2 138.8

Note: In estimation, the portfolio scope measures are adjusted by the number of product-market segment and the analysis takes log of Size, Sales, Cost, Profit,and Primary Patents and Complementary Patents. Also, Introduction and Pioneering Intensities are adjusted by industry averages.


Golder and Tellis, 1993; Pauwels et al., 2004). The firmvaluation implications of product portfolio strategy havereceived only limited research attention to date. Byexamining multiple time horizons and relevant firm per-formance metrics, one can better understand the short-lived (temporary) and long-lived (persistent) effects ofproduct portfolio strategy decisions on firm performance.Thus, this study considers three measures of financialperformance—sales, profit, and return.

Sales has been proposed as one of the most importantmeasures of business performance on which managersshould focus (Reichheld, 2003), and it is a measure of firmperformance that is often closely associated with product-level decisions. Similarly, gross profit (i.e., sales revenueminus cost of selling) is an indicator of the firm’s valuechain, specifically measuring a firm’s ability to convertinputs into valuable outputs (Bell, Deighton, Reinartz,Rust, and Swartz, 2002; Ittner and Larcker, 1998). Salesand profit are both short-term indicators of firm perfor-mance commonly used in firm-level studies of innovation.

To examine long-term financial performance, thestudy considers a firm’s market-adjusted abnormal return(i.e., the rate of return minus the average expected rate ofreturn of the stock market). Return is forward looking andcaptures the net present value of future rents afteraccounting for risk. Return is the most widely used metricin the finance literature to measure a firm’s financialperformance (e.g., Kothari and Warner, 2006). Becauseshareholders are the owners of the firm, they are animportant constituency whose interests should beincluded in business decisions (Day and Fahey, 1988). Asa result, it is a common practice that boards of directorslink a large portion of top executives’ compensation tothe firm’s stock return (e.g., Guay, 1999). Certainly, firmperformance in terms of sales and profits are key sourcesof increasing shareholder return. Likewise, the strategicchoices reflected by the product portfolio serve as impor-tant additional contributors to shareholder returns.

Measurement: Product Portfolio Strategy

The study characterizes the product portfolio in terms ofoutcomes of the product development and market entrystrategies. In doing so, it is important to consider thediscontinuity in both marketing and technology processes(e.g., Garcia and Calantone, 2002). The analysis alsoclosely follows the four innovation categories, proposedby Abernathy and Clark (1985). For a given year, basedon the product development strategy, a firm has a set ofnew products (NewProd) that are introduced in the yearand a set of existing products (MatureProd) in its portfo-

lio. Similarly, depending on its market entry strategy,the product portfolio can be decomposed. A product isintroduced either as first in the product-market segmentas a pioneer in the market (Leading) or late in the segmentas a follower relative to the leader (Following). Inaddition, the product portfolio is further decomposed bycharacterizing the unique combinations of product devel-opment and market entry strategies (i.e., interactions):Leading-Mature, Leading-New, Following-Mature, andFollowing-New. These measures capture the product-level direct effects of product development and marketentry strategy. These measures are also remotely relatedwith Abernathy and Clark’s (1985) categorizations:architectural (cf. Leading-New), niche creation (cf.Following-New), regular (cf. Following-Mature),and revolutionary (cf. Leading-Mature) innovations.Figure 3 illustrates this decomposition of the productportfolio strategy. Each product portfolio componentreflects a combination of product development andmarket entry strategies. In the empirical analysis, the fourportfolio strategy component measures are adjusted bythe total number of product-market segments (NMarket)so that the measures represent portfolio strategies in eachmarket segment.

The study also measure two additional indicatorsof product portfolio strategic choices: introductionintensity and pioneering intensity (see Figure 3). Whilethe product portfolio components described abovecapture the product-level innovativeness, the intensitymeasures capture the firm-level innovativeness of theportfolio.

Introduction intensity is defined as the rate of newproduct introduction in the product portfolio, and pio-neering intensity is defined as the rate of first-to-market

launches in the product portfolio, that is, IN

NIit

Nit

it

= and

Product Development Strateg y

Mature (NOit) New (NN

it)

Market Entry

Strateg y

Leading (NF

it) Leading-Mature

(NFOit)

Leading-New (NFN

it)

Following (NL

it) Following-Mature

(NLOit)

Following-New (NLN

it)

Product Portfolio Components (Nit)

Introduction Intensity (IIit) = New/(New + Mature)

Pioneering Intensity (IPit) = Leading/(Leading + Following)

Figure 3. Product Portfolio Strategy


IN

NP

it

Fit

it

= , where, Nit is the number of total product

portfolio, N Nit is the number of new products, and N F

it isthe number of first-to-market products in firm i’s portfo-lio at time t.

In this analysis, introduction intensity measure isweighted by time since introduction, whereas the pio-neering intensity measure is not weighted. Because pio-neering intensity captures a product’s order of entry, thefact of “being first” in the market does not change.However, because introduction intensity is related with afirm’s product portfolio freshness, it varies over time.That is, this year’s new product will be a mature productnext year. And each product has different freshness (e.g.,1-year-old versus 10-year-old products). By taking intoaccount the time since introduction, the analysis allevi-ates this potential concern in the measurement. The paperalso estimated the model using unweighted introductionintensity measure, resulting in similar conclusions. Inpractice, it is not clear how to define the “freshness” ofnew products (e.g., introduced this year, or introduced inlast three years). By taking into account the time sinceintroduction, the weighted measure is more robust to thevarious definitions of “new” products. In addition, themodel adds quadratic terms of introduction intensity andpioneering intensity in the empirical model in order toexamine the nonlinear (i.e., diminishing) effects of theintensities. These terms allow us to incorporate the ideaof balancing. The medical device industry data start from1991. The analysis accounts for the fact that in the earlyperiod of the data, most products are first in the product-market segment without many existing competitors. Inorder to make the inference time invariant, IntroductionIntensity and Pioneering Intensity are adjusted by entiremedical device industry average to control any bias asso-ciated with a specific time window.

Empirical Model

To empirically investigate the effects of the strategiccomponents of the product portfolio on the performanceof firm i in year t, the following fixed- and random-effectmodel is constructed:

Performance PortfolioStrategy

Control

it it k kitk

m mitm

= +

+ +

∑∑

α β

γ εε ε σεit it N, ~ ,0( )(1)

In the empirical analyses, the analysis examines bothshort-term performance (Sales and Profit) and long-term

performance (Return). To control the spurious correla-tions between firm size and the performance variables,the empirical model uses the log of Sales and Profit. TheReturn is obtained after adjusting any changes in thenumber of stocks (e.g., stock split).

In addition to product portfolio strategy variables, themodel included two technological capability measures:Primary Patents (i.e., the number of cumulative patentsin medical device industry) and Complementary Patents(i.e., the number of cumulative patents in other indus-tries). Although the technological capabilities are realizedthrough product portfolio, there might be direct effects onfirm performance as well (e.g., Wang and Chen, 2010).The analysis further controls firm- and competitor-levelvariables: firm size (Size) and competition (CProducts).Firm size (Size) is measured by taking the log of thefirm’s total asset size. Firm size is a good proxy measurefor a firm’s previous performance and current resources,thus separating any size-related effect in the model (cf.Sutton, 1998). The analysis also measures a firm’saverage competitive environment by adding CProducts(average number of competing products), which is calcu-lated within a product-market segment and then averagedacross all product-market segments in which a firm com-petes (cf. Stavins, 1995). In addition to these controlvariables, the model adds a lagged performance variable(i.e., lagged sales, profit, or return) to further examine anyfirm-specific persistent effects (cf. Geroski, Machin, andVan Reenen, 1993). Consistent with the finance literatureand following the spirit of Fama and French (1993; three-factor model) and Carhart (1997; four-factor model), themodel also controls for the weighted-average marketreturn, firm size, short-term performance, and laggedreturn in the return model.

The model also includes a random-effects specifica-tion in each of the equations to account for potentialfirm-specific heterogeneity. The model further controlsfor any systematic changes over time and across indus-tries by including fixed effects in each equation. Tocontrol for year-specific heterogeneity, the analysis addedyear dummies for each year. Lastly, the analysis addedsubindustry dummies to control for segment-specific het-erogeneity. The fixed effects capture average differencesacross the years of estimation and seven industries. Afteradding both fixed and random effects, the intercept ismodeled as:

α α α α υ

υ συ

it t tt

k kk

i

i

Year Industry

N

= + + +

( )= =∑ ∑

2

14

2

7

0

,

~ ,

(2)


Using a series of random-effect and fixed-effect regres-sion models for time-series cross-sectional data, theeffects of a set of product portfolio strategy measures andcontrol variables on a set of firm performance measuresare estimated. This combination of fixed-effects andrandom-effects corrections addresses violations of stan-dard regression assumptions in cross-sectional time-series data (e.g., Greene, 2003). The model was estimatedusing a restricted maximum likelihood estimation forvariance components and generalized least squares forfixed-effect parameters (Bryk and Raudenbush, 1992).

Estimation Results

The estimation results for the sales, profit, and returnmodels are reported in Tables 2–4, respectively. Forbenchmarking purposes, the discussion begins with theestimation results for the models involving only firm-level intensities (model 1) or product-level developmentstrategy (model 2). Model 3 considers only firm-levelintensities and product development strategy. The estima-tion results for the complete portfolio strategy involvingcombined product development and market entry strate-

gies (model 4: full model) provide a deeper understand-ing. Overall, the empirical estimation results indicate thatthe measures of product portfolio strategy explain signifi-cant variance in firms’ future business performance.

Sales Model

The estimation results for the sales equation are presentedin Table 2. The estimated model fits the data well, and allsignificant coefficients have the expected signs. In addi-tion, the variance of the random effect is significant,implying that the average sales level is heterogeneousacross firms.

First, firm sales exhibit a state-dependent persistenceacross all models. That is, the lagged firm sales havesignificant positive effects on the current sales level. Andfirm size (Size) is positively associated with sales, imply-ing more firm-level resources lead to higher sales.However, the number of competing products (CProducts)is significantly related to firm sales only in model 4. Thissuggests that the competing products help to expand theentire market. In addition, technological capability inmedical device domain (Primary Patents) is significantly

Table 2. Estimation Result of Sales Model (Standard Errors in Parentheses)

(log) Sales

Model 1 Model 2 Model 3 Model 4

Portfolio strategyMatureProd .080 (.099) −.121 (.108)

Leading-Mature .310 (.158)*Following-Mature −.059 (.110)

NewProd −.026 (.095) .222 (.111)*Leading-New .723 (.169)**Following-New .239 (.111)*

Introduction intensity −.299 (.083)** −.793 (.177)** −.804 (.182)**Introduction intensity2 .178 (.098) .349 (.113)** .382 (.116)**

Pioneering intensity −.049 (.115) .001 (.115) −.434 (.159)**Pioneering intensity2 −.128 (.197) −.122 (.196) .242 (.216)

Control variablesPrimary patents .164 (.033)** .178 (.032)** .160 (.033)** .144 (.033)**Complementary patents .077 (.035)* .089 (.035)* .070 (.035)* .067 (.035)Primary patents × complementary patents −.004 (.009) −.008 (.009) −.002 (.009) .004 (.009)Lagged (log) sales .056 (.010)** .051 (.010)** .059 (.010)** .059 (.010)**Size .595 (.020)** .595 (.020)** .594 (.020)** .590 (.020)**CProducts .013 (.008) .014 (.008) .012 (.008) .018 (.008)*

Random effects Σ(1,1) = .752 (.113)** Σ(1,1) = .779 (.116)** Σ(1,1) = .753 (.113)** Σ(1,1) = .702 (.105)**Variance component σ2 = .097 (.004)** σ2 = .098 (.004)** σ2 = .096 (.004)** σ2 = .095 (.004)**Fit statistics −2 Res LL: 1126 −2 Res LL: 1135 −2 Res LL: 1121 −2 Res LL: 1112

AIC: 1130 AIC: 1139 AIC: 1125 AIC: 1116BIC: 1136 BIC: 1145 BIC: 1131 BIC: 1121

* p < .05; ** p < .01.Note: For brevity, intercept, year, and industry-specific fixed-effects estimates are dropped.AIC, Akaike information criterion; BIC, Bayesian information criterion; Res LL, restricted log-likelihood.


positive in all models even after controlling product port-folio strategy variables. On the other hand, technologicalcapability in other domains (Complementary Patents) isno longer significant once product development andmarket entry strategies are fully considered. The interac-tions are not significant in any model.

While the estimation results of control variables arevery reasonable and reassuring, the effects of the productportfolio strategy are of central interest in this study.Specifically, after controlling firm-level intensities(model 3), the number of new products in the portfolio(NewProd) is positively associated with sales. However,the number of products in the existing product portfolio(MatureProd) does not have a significant effect on sales.That is, firms offering a greater number of new productintroductions tend to have higher sales volume.

To take into account the market entry strategy, whenthe product portfolio strategy was further decomposed(model 4), similar positive effects for the number of newproducts in the portfolio was found. Both Leading-Newand Following-New portfolio components are positivelyassociated with sales. Also, the mature product portfoliocomponents have differential effects on sales. Leading-

Mature is positively associated with sales, but Following-Mature is not significantly associated with firm sales.Even though aggregated MatureProd is not significantlyassociated with firm sales, the leading mature productportfolio (Leading-Mature) still increases current sales.That is, the positive effect of new product introductiondisappears over time if the product is just following othermarket leaders. This implies that the pioneering advan-tage persists over time in this industry. Also, among thedecomposed portfolio strategy components, Leading-New has the strongest effect (.723 compared with .310for Leading-Mature and .239 for Following-New). Theauthors conjecture that this is because the creation of anew market segment is related to a new solution forongoing medical needs. Therefore, the new solution isimmediately rewarded by the market and it is persistent.

In the full model (model 4), Introduction Intensityshows a convex relationship with firm sales, that is, thelinear term is negative, and the quadratic term is positive.That is, firms can increase the sales by focusing oneither mature products or new products. Firms with moreexperience and proven products in the portfolio enjoyhigher sales through incremental improvement in more

Table 3. Estimation Result of Profit Model (Standard Errors in Parentheses)

(log) Profits


Portfolio strategyMatureProd .074 (.107) −.080 (.118)

Leading-Mature .322 (.163)*Following-Mature −.045 (.120)

NewProd −.028 (.103) .136 (.122)Leading-New .523 (.188)**Following-New .141 (.122)

Introduction intensity −.283 (.094)** −.593 (.199)** −.544 (.206)**Introduction intensity2 .178 (.109) .283 (.125)* .283 (.128)*

Pioneering intensity −.016 (.129) .085 (.035)* .016 (.130) −.357 (.177)*Pioneering intensity2 −.359 (.222) .060 (.036) −.367 (.222) −.036 (.247)

Control variables .011 (.009)Primary patents .075 (.036)* .069 (.012)** .075 (.036)* .063 (.036)Complementary patents .053 (.036) .688 (.023)** .049 (.036) .044 (.036)Primary patents × complementary patents .013 (.009) .008 (.009) .014 (.010) .018 (.010)Lagged (log) profits .074 (.012)** .076 (.012)** .072 (.012)**Size .683 (.023)** .682 (.023)** .675 (.023)**CProducts .005 (.009) .005 (.009) .013 (.009)

Random effects Σ(1,1) = .381 (.064)** Σ(1,1) = .389 (.065)** Σ(1,1) = .382 (.064)** Σ(1,1) = .379 (.062)**Variance component σ2 = .115 (.006)** σ2 = .117 (.006)** σ2 = .115 (.006)** σ2 = .114 (.006)**Fit statistics −2 Res LL: 1107 −2 Res LL: 1117 −2 Res LL: 1109 −2 Res LL: 1104


* p < .05; ** p < .01.Note: For brevity, intercept, year, and industry-specific fixed-effects estimates are dropped.AIC, Akaike information criterion; BIC, Bayesian information criterion; Res LL, restricted log-likelihood.


mature products. The authors conjecture that these firmsmight rely on their external resources to obtain the nec-essary innovativeness (cf. open innovation—Chesbrough,2003). Also, a product portfolio with entirely new pro-ducts can increase firm sales as well by keeping up thehigh intensity of new product introduction. Firms withaverage intensity might be stuck in the middle. On theother hand, even though the number of pioneering pro-ducts (Leading-Mature and Leading-New) increases firmsales, firms with a higher Pioneering Intensity hurts firmsales. This implies that the firm-level innovativeness doesnot necessarily translate into sales without actually intro-ducing pioneering products in marketplace.

Profit Model

The estimation results for the profit equation are pre-sented in Table 3. The estimated model fits the data well,and all significant coefficients have the expected signs. Inaddition, the variance of the random effect is significant,

implying that the average profit level is heterogeneousacross firms. In general, the estimation results across allmodels are very similar to those of sales equation. Thissuggests that the effects of product portfolio strategy areconsistent even after controlling for cost. However, in thefull model (model 4), a notable difference from the salesmodel is that the Following-New portfolio component isnot positively associated with firm profit anymore. Thisimplies that, although new products increase firm sales,the Following-New component of the portfolio does notgenerate enough profit margin because of the additionalcosts. In addition, technological capabilities are no longersignificant in the full model.

Return Model

While the sales and profit models show the short-termrelationships between product portfolio strategy and firmperformance, the return model provides a long-term per-spective because the stock return is calculated from the

Table 4. Estimation Result of Return Model (Standard Errors in Parentheses)

Return


Portfolio strategyMatureProd −.012 (.133) −.047 (.137) −.318 (.183)

Leading-Mature −.104 (.145)Following-Mature

NewProd .071 (.137) .122 (.169)Leading-New −.090 (.201)Following-New .210 (.177)

Introduction intensity .221 (.167) −.024 (.259) −.241 (.276)Introduction intensity2 −.200 (.195) −.111 (.208) −.043 (.213)

Pioneering intensity .244 (.148) .279 (.151) .479 (.175)**Pioneering intensity2 −.485 (.266) −.515 (.269) −.651 (.276)*

Control variablesPrimary patents .043 (.025) .036 (.025) .043 (.026) .046 (.026)Complementary patents .007 (.024) .001 (.024) .007 (.024) .008 (.024)Primary patents × complementary patents −.004 (.006) −.003 (.006) −.004 (.006) −.004 (.006)Market return .669 (.123)** .654 (.122)** .678 (.123)** .706 (.123)**Lagged return −.166 (.034)** −.164 (.034)** −.165 (.034)** −.168 (.034)**Log(Profit) .153 (.039)** .149 (.039)** .151 (.039)** .157 (.039)**Size −.163 (.041)** −.159 (.041)** −.159 (.041)** −.157 (.041)**CProducts .008 (.008) .013 (.008) .010 (.008) −.001 (.010)

Random effects Σ(1,1) = 0 (0) Σ(1,1) = 0 (0) Σ(1,1) = 0 (0) Σ(1,1) = 0 (0)Variance component σ2 = .399 (.020)** σ2 = .400 (.020)** σ2 = .399 (.020)** σ2 = .398 (.020)**Fit statistics −2 Res LL: 1596 −2 Res LL: 1597 −2 Res LL: 1599 −2 Res LL: 1599


* p < .05; ** p < .01.Note: For brevity, intercept and industry-specific fixed-effects estimates are dropped. The paper reports results from value-weighted market return. The paperexamined both value-weighted and equally weighted market returns, resulting in a similar set of results.AIC, Akaike information criterion; BIC, Bayesian information criterion; Res LL, restricted log-likelihood.


discounted future cash flow. In the return model, to beconsistent with the finance literature, the model adds twoadditional control variables: weighted market return andshort-term firm performance (Carhart, 1997; Fama andFrench, 1993). We examined both value-weightedand equally weighted market returns, resulting in asimilar set of results. To capture short-term performance,we also examined both profit and sales, leading to similarresults.

The estimation results for the return equation are pre-sented in Table 4. The estimated model fits the data well.Note that the return model added the log of firm profit tocapture the effect of current performance on the stockreturn. Across all models, the variance of the randomeffect is not significant, implying that the average market-adjusted return is homogeneous across firms. Asexpected, firm profit has a positive effect on return. Con-sistent with the finance literature, firm size is negativelyassociated with the market-adjusted return. Also, thenegative effect of the lagged return implies that a firm’sstock return converges to market return over time, whichis again consistent with the finance literature.

Contrary to the sales and profit models, the product-level portfolio strategy component variables are not sig-nificant in any model. In fact, only Pioneering Intensityhas a positive and diminishing effect in model 4 (i.e., thelinear term is positive and the quadratic term is negative).That is, the financial market only rewards a firm’s overallcapability to deliver new products first in the market-place. This also suggests that the portfolio componentsonly have indirect effects on the market-adjusted returnvia a firm’s current profit (or sales). Thus, although thePioneering Intensity does not have a positive immediateeffect on firm’s sales/profit, it has a long-term forward-looking effect on firm’s performance by achieving afavorable position in the marketplace. Note that in thesales and profit models, MatureProd is not significantlyassociated with firm performance in general, even thoughNewProd increases firm performance. However, onlyLeading-Mature portfolio has a positive effect on a firm’sshort-term performance. In other words, pioneering prod-ucts have lagged positive effects, but these effects are notfully reflected in short-term firm performance. Thus, theforward-looking financial market rewards this overlookedpositive effect.

Drivers of Innovation: Innovation Model

In previous sections, we examined the financial perfor-mance of product portfolio as defined product develop-ment and market entry strategies. In fact, these strategies

depend on a firm’s capabilities. Thus, we now furtherinvestigate the relationship between technological capa-bilities and product portfolio, that is, Leading-Mature,Leading-New, Following-Mature, and Following-New tobetter understand the drivers of these product portfolios.

Innovation Model

In order to capture a firm’s technological capabilities, theanalysis uses the patent counts to measure the inventiveactivities (e.g., Griliches, 1990, 1998). Patent data are usedextensively as an indicator of technological inventionproductivity in various contexts (e.g., Basberg, 1987;Katila and Ahuja, 2002; Lin, Chen, and Wu, 2006; Narin,Noma, and Perry, 1987). The measure was constructedfrom the USPTO on the number of patents. Becausea firm’s capabilities are from accumulated knowledgebundles (Day, 1990), the analysis uses a cumulativenumber of patent applications as a measure of technologi-cal capabilities. In the empirical analysis, this measure waslog-transformed and temporally lagged to address poten-tial spurious correlations and reverse causality.

A firm could develop its capability in a same techno-logical domain or in a wide variety of other technologicaldomains. Thus, the analysis decomposes all patents intotwo categories: primary (Primary Patents) and comple-mentary patents (Complementary Patents). Firms focus-ing on the primary technology domain (i.e., medicaldevice industry) are familiar with the types of problemsand easily understand the new knowledge in the sametechnological domain. And the innovation process ismore predictable and efficient, facilitating exploitation ofwhat is already known (Makri et al., 2010; Nonaka et al.,1996). On the other hand, complementary technologicalknowledge (i.e., Complementary Patents) helps explora-tion process through new experimentations (March,1991), extending the scope of knowledge applications(Makri et al., 2010). In fact, integrating complementaryknowledge increases the new product introductions(Rothaermel, Hitt, and Jobe, 2006). For the empiricalanalysis, the primary patent class-subclasses for medicaldevice industry are defined by USPTO (2005), and allother patents are considered as complementary.

In addition to technological capability, the modelcontrols for firm size (Size), measured by taking the log ofthe firm’s total asset size. Firm size is a good proxymeasure for a firm’s current resources, thus separatingany size-related effect in the model (cf. Sutton, 1998).Di Benedetto et al. (2008) explicitly considered a widevariety of strategic capabilities to explain introductions ofradical innovations. Firm size could be considered as a


proxy measure of these capabilities. The model furthercontrols for any systematic changes over time and acrossindustries by including fixed effects. To control for year-specific heterogeneity, year dummies for each year wereadded. Subindustry dummies were also added to controlfor segment-specific heterogeneity. The fixed effectscapture average differences across the years of estimationand seven industries.

To empirically investigate the effects of the techno-logical capabilities on product portfolio, the modelemploys a negative binomial specification as the depen-dent variables are count data, that is, nonnegative integerswith frequent zeros. In the count data, a Poisson specifi-cation is common, which assumes a variance being equalto the mean. However, the data show substantial differ-ences in the means and variances of product portfoliowithin firms. The negative binomial specifications allowfor a firm-specific mean as well as a firm-specificvariance-to-mean ratio. The negative binomial specifica-tion will address the overall overdispersion issues (e.g.,too many zeros).

Estimation Results

The estimation results for innovation model are presentedin Table 5. The estimated model fits the data well, andall significant coefficients have the expected signs. Asexpected, a firm’s technological capabilities indeedfacilitate the product portfolio. Key findings are asfollows.

First, the introduction of pioneering products (i.e.,Leading-New and Leading-Mature) is driven by the inter-action of Primary Patents and Complementary Patents(i.e., the interaction terms in leading-new and leading-mature equations are significantly positive). This implies

that the full integration of primary and complementaryknowledge is necessary to develop and maintain the pio-neering innovations. Thus, the Pioneering Intensity is theresult of this integrated knowledge. In addition, to keepthe mature pioneering products in the portfolio, a widevariety of complementary knowledge is also needed toextend the scope of knowledge applications (i.e., thecoefficient of Complementary Patents in Leading-Matureequation is significantly positive).

Second, the introduction of following products (i.e.,Following-New and Following-Mature) is driven by theprimary technological capabilities (i.e., the coefficients ofPrimary Patents in following-new and following-matureequations are significantly positive). That is, understand-ing the own technological domain is an important driverof nonpioneering product portfolio. Also, this primarytechnological capabilities alone can increase the Intro-duction Intensity. In addition, to keep the mature follow-ing products in the portfolio, additional complementaryknowledge is needed to extend the applications of newproducts (i.e., the interaction term in following-matureequation is significantly positive).

These results confirm that technological capabilitiesindeed drive the product portfolio decisions. Combinedwith performance model results, the effects of techno-logical capabilities on firm performance are mediated bythe realized product portfolio strategies.

Conclusion, Implications, andFuture Research

Innovation is both the creator and destroyer of industriesand corporations. Today, when competitiveness hinges onthe ability to develop or adapt new technologies in prod-ucts, services, and process, understanding the dynamics

Table 5. Estimation Results of Innovation Model

Explanatory Variables

Dependent Variable

Leading-New Leading-Mature Following-New Following-Mature

Primary patents −.027 (.085) .048 (.043) .255 (.078)** .131 (.045)**Complementary patents −.138 (.088) .138 (.046)** −.085 (.084) −.019 (.049)Primary patents × complementary patents .046 (.019)* .023 (.011)* .009 (.019) .029 (.011)*Size .501 (.056)** .347 (.026)** .533 (.051)** .371 (.028)**Scale parameter 2.447 (.281) .976 (.054) 2.604 (0) .922 (0)Fit statistics −2 LL = 1768 −2 LL = 5053 −2 LL = 2357 −2 LL = 4160

BIC = 1932 BIC = 5217 BIC = 2521 BIC = 4324χ2 = 1148 χ2 = 1129 χ2 = 1141 χ2 = 937

* p < .05; ** p < .01.Note: For brevity, intercept, year, and industry-specific fixed-effects estimates are dropped. Patents variables are lagged, and size is log-transformed. Thepaper also estimated the models without interaction terms, resulting in similar main effects.AIC, Akaike information criterion; BIC, Bayesian information criterion; LL, log-likelihood.


of industrial innovation and the related rewards and risksis essential for survival and success. Strategic leadership,investment, and creativity in product development mustbe aligned if firms are to build and sustain competitivesuccess. To better understand the impact of product port-folio decisions, the paper empirically analyzed the effectsof the product portfolio components on firms’ financialperformance.

The key results are as follows. First, a large productportfolio helps a firm’s financial performance. We foundthat new products are positively associated with sales andprofits. However, each component of a portfolio servesdifferent roles. In particular, the pioneering new productshave strongest impacts on short-term performances, andnonpioneering mature products do not significantly con-tribute to a firm’s financial performance. This result sug-gests that it is important to consider both productdevelopment and market entry strategies to understandthe financial impacts of product portfolio. Second, theresults indicate a persistent first-mover advantage. Thatis, first-to-market new products yield not only an imme-diate effect, but also persistent long-term effects. That is,the positive effect of a new product disappears over timeif the product is not a pioneering product. This suggeststhat it is important to be first in the market even thoughthere may be short-term, temporary losses. Althoughthere are some debates on first-mover advantages, theresults favor the advantages in medical device industry.Third, the results suggest the need to balance between thenumber of “mature” products and “new” products. Also,firms need to balance the number of “first-to-market”products and “late-entered” products. Because a new orpioneering product requires more firm resources, it hurtsother mature or following products in the portfolio. Thus,without support from mature or follower products, newproducts and pioneering products alone may not increasefirm sales or profit. Fourth, from a long-term perspective,only pioneers receive rewards after controlling for short-term performance. That is, the financial market rewardsonly a firm’s overall capability to deliver new productsfirst in the marketplace (i.e., Pioneering Intensity). Inaddition, the estimation results do not find long-termeffects of all other product portfolio strategies, suggestingthat their long-term impacts are only indirect via short-term firm performance. Thus, short-term performance(i.e., sales and profits) are mainly driven by product-level innovativeness, whereas firm-level innovativenessenhances forward-looking long-term performance (i.e.,return). This finding suggests that it is important toexamine both short-term and long-term financial perfor-mance measures. Fifth, pioneering new products are

driven by integrating both primary and complementarytechnological capabilities. And nonpioneering new prod-ucts are mainly driven by the capabilities in primarytechnology domain. By investigating the relationshipbetween technological capabilities and innovation strate-gies, one can better understand their financial impacts.

This study has several key implications for practice.The results provide important insight into the relativevalue and timing of return on investment in radical versusincremental innovation and alternative market entry strat-egies. By understanding the performance trade-offs ofthese different factors in the short and long term, one candevelop better guidelines for optimizing innovation strat-egies, and their dependence on both external and internalenvironmental conditions. An advantage of conductingthis study within an industry characterized by a techno-logically dynamic market in which innovation plays acritical role in continued industry growth is that thestrengths and vulnerabilities of other prominent indus-tries can easily be recognized by analogy. The findingsshould be broadly generalizable to technology-basedindustries with similar characteristics, for example, lifesciences, computing, telecommunications, electronics,and chemicals.

For future research, one could supplement theseobjective data sets with qualitative data from industrytrade association publications and expert opinions frommanagers. This will enable us to develop new measures ofthe degree of technical innovations and further investigateasymmetric effects of different types of innovation (e.g.,radical versus incremental innovation). For another pos-sible future research, Rothaermel et al. (2006) found thatfirms with integrated complementary knowledge frominternal and external sources introduced more new prod-ucts in the market. As Chesbrough (2003) has coined theterm “open innovation,” an open innovation approachrefers to systematically relying on the wide range ofinternal and external technology sources to build a firm’sdynamic capabilities (Lichtenthaler, 2008). In fact, manyfirms consider acquiring technology to complement theinternal technological competence (Lane and Lubatkin,1998; Pisano, 1990; von Hippel, 1988).

References

Abernathy, W. J., and K. B. Clark. 1985. Innovation: Mapping the winds ofcreative destruction. Research Policy 14 (1): 3–22.

Abernathy, W. J., and J. M. Utterback. 1978. Patterns of innovation intechnology. Technology Review 80 (7): 40–47.

Ailawadi, K., R. P. Dant, and D. Grewal. 2004. The difference betweenperceptual and objective performance measures: An empirical analy-sis. Report 04-103. Cambridge, MA: Marketing Science Institute.

Ansoff, H. I. 1988. The new corporate strategy. New York: Wiley.


Atuahene-Gima, K. 1995. An exploratory analysis of the impact of marketorientation on new product performance: A contingency approach.Journal of Product Innovation Management 12: 275–93.

Bananno, G. 1987. Location choice, product proliferation, and entry deter-rence. Review of Economic Studies 54 (3): 47–64.

Barney, J. B. 1991. Firm resources and sustained competitive advantage.Journal of Management 17: 139–57.

Basberg, B. L. 1987. Patents and the measurement of technological change:A survey of the literature. Research Policy 16: 131–41.

Baumol, W., J. Panzar, and R. Willig. 1982. Contestable markets and thetheory of industry structure. San Diego, CA: Harcourt Brace Jovaovich.

Bayus, B. L., G. Erickson, and R. Jacobson. 2003. The financial rewards ofnew product introductions in the personal computer industry. Manage-ment Science 49 (2): 197–210.

Bell, D., J. Deighton, W. J. Reinartz, R. T. Rust, and G. Swartz. 2002. Sevenbarriers to customer equity management. Journal of Service Research 5(1): 77–85.

Benson, B. 1990. Increasing product variety and rising prices. Review ofIndustrial Organization 5 (1): 31–52.

Betz, F. 1987. Managing technology: Competing through new ventures,innovation, and corporate research. Upper Saddle River, NJ: PrenticeHall.

Black, J. A., and K. B. Boal. 1994. Strategic resources: Traits, configura-tions and paths to sustainable competitive advantage. Strategic Man-agement Journal 15: 131–48.

Bordley, R. 2003. Determining the appropriate depth and breadth of afirm’s product portfolio. Journal of Marketing Research 40 (February):39–53.

Brander, J., and J. Eaton. 1984. Product line rivalry. American EconomicReview 74 (June): 323–34.

Bryk, A. S., and S. W. Raudenbush. 1992. Hierarchical linear models:Applications and data analysis methods. Thousand Oaks, CA: Sage.

Carhart, M. M. 1997. On persistence in mutual fund performance. Journalof Finance 52 (1): 57–82.

Cefis, E., and O. Marsili. 2006. Survivor: The role of innovation in firms’survival. Research Policy 35 (5): 626–41.

Chandy, R. K., and G. J. Tellis. 1998. Organizing for radical productinnovation: The overlooked role of willingness to cannibalize. Journalof Marketing Research 35 (November): 474–87.

Chandy, R. K., and G. J. Tellis. 2000. The incumbent’s curse? Incumbency,size, and radical product innovation. Journal of Marketing 64 (July):1–17.

Chaney, P. K., T. M. Devinney, and R. S. Winer. 1991. The impact of newproduct introductions on the market value of firms. Journal of Business64 (4): 573–610.

Chesbrough, H. 2003. Open innovation: The new imperative for creatingand profiting from technology. Boston, MA: Harvard Business SchoolPress.

Clark, K. 1987. Investment in new technology and competitive advantage.In The competitive challenge, ed. D. J. Teece, 59–82. Grand Rapids,MI: Harper & Row.

Cockburn, I., and Z. Griliches. 1988. Industry effects and appropriabilitymeasures in the stock market’s valuation of R&D and patents.American Economic Review 78: 419–23.

Cohen, M. A., J. Eliashberg, and H. Tech-Hua. 1996. New product devel-opment: The performance and time-to-market tradeoff. ManagementScience 42 (February): 173–86.

Cohen, W. M., and D. A. Levinthal. 1990. Absorptive capacity: A newperspective on learning and innovation. Administrative Science Quar-terly 35: 128–52.

Connor, J. 1981. Food product proliferation: A market structure analysis.American Journal of Agricultural Economics 21 (November): 606–17.

Cooper, R. G., S. J. Edgett, and E. J. Kleinschmidt. 2001. Portfolio man-agement for new products (2nd ed.). Cambridge, MA: Perseus Books.

Damanpour, F. 1991. Organizational innovation: A meta-analysis of effectsof determinants, and moderators. Academy of Management Journal 34(3): 555–90.

Danneels, E. 2004. Disruptive technology reconsidered: A critique andresearch agenda. Journal of Product Innovation Management 21: 246–58.

Day, G. 1981. The product life cycle: Analysis and application issues.Journal of Marketing 45 (Autumn): 60–67.

Day, G., and L. Fahey. 1988. Valuing market strategies. Journal of Mar-keting 52 (3): 45–57.

Day, G. S. 1990. Market driven strategy: Processes for creating value. NewYork: Free Press.

Di Benedetto, C. A., W. S. DeSarbo, and M. Song. 2008. Strategic capa-bilities and radical innovation: An empirical study in three countries.IEEE Transactions on Engineering Management 55 (3): 420–33.

Dosi, G. 1982. Technological paradigms and technological trajectories.Research Policy 11 (3): 147–62.

Dutta, S., O. Narasimhan, and S. Rajiv. 2005. Conceptualizing and mea-suring capabilities: Methodology and empirical application. StrategicManagement Journal 26: 277–85.

Ettlie, J. E., W. P. Bridges, and R. D. O’Keefe. 1984. Organizationalstrategy and structural differences for radical versus incremental inno-vation. Management Science 30: 682–92.

Fama, E. F., and K. French. 1993. Common risk factors in the returns onstocks and bonds. Journal of Financial Economics 33 (1): 3–36.

Fleming, L. 2001. Recombinant uncertainty in technological search. Man-agement Science 47: 117–32.

Fleming, L., and O. Sorenson. 2001. Technology as a complex adaptivesystem: Evidence from patent data. Research Policy 30 (7): 1019–39.

Freeman, J. H., and M. T. Hannan. 1983. Niche width and the dynamics oforganizational populations. American Journal of Sociology 88: 1116–45.

Furman, J. L., M. E. Porter, and S. Stern. 2002. The determinants ofnational innovative capacity. Research Policy 31 (6): 899–933.

Garcia, R., and R. Calantone. 2002. A critical look at technological inno-vation typology and innovativeness terminology: A literature review.Journal of Product Innovation Management 19 (2): 110–32.

Geroski, P., S. Machin, and J. Van Reenen. 1993. The profitability ofinnovating firms. RAND Journal of Economics 24 (2): 198–211.

Ghemawat, P., and P. Del-Sol. 1998. Commitment versus flexibility? Cali-fornia Management Review 40 (4): 26–42.

Golder, P. N., and G. J. Tellis. 1993. Pioneer advantage: Marketing logic ormarketing legend. Journal of Marketing Research 30 (May): 158–70.

Greene, W. H. 2003. Econometric analysis (5th ed.). Upper Saddle River,NJ: Prentice Hall.

Griliches, Z. 1990. Patent statistics as economic indicators: A survey.Journal of Economic Literature. 28: 1661–707.

Griliches, Z. 1998. R&D and productivity: The econometric evidence.Chicago, IL: University of Chicago Press.

Guay, W. R. 1999. The sensitivity of CEO wealth to equity risk: An analysisof the magnitude and determinants. Journal of Financial Economics 53(1): 43–71.

Hambrick, D. C. 1982. Environmental scanning and organization strategy.Strategic Management Journal 3 (2): 159–74.

Harabi, N. 1995. Appropriability of technical innovations: An empiricalanalysis. Research Policy 24: 981–92.

Hauser, J., G. J. Tellis, and A. Griffin. 2006. Research on innovation: Areview and agenda for marketing science. Marketing Science 25 (6):687–717.

Helfat, C. E., and M. A. Peteraf. 2003. The dynamic resource-based view:Capability lifecycles. Strategic Management Journal 23: 997–1010.


Ittner, C., and D. Larcker. 1998. Are nonfinancial measures leading indi-cators of financial performance? An analysis of customer satisfaction.Journal of Accounting Research 36 (Suppl.): 1–35.

Kanter, R. M. 1983. The change masters. New York: Simon & Schuster.

Katila, R., and G. Ahuja. 2002. Something old, something new: A longitu-dinal study of search behavior and new product introduction. Academyof Management Journal 45 (6): 1183–94.

Kerin, R. A., P. R. Varadarajan, and R. A. Peterson. 1992. First-moveradvantage: A synthesis, conceptual framework, and research proposi-tions. Journal of Marketing 56 (October): 33–52.

Klette, T. J., and S. Kortum. 2004. Innovating firms and aggregate innova-tion. Journal of Political Economy 112 (5): 986–1018.

Kothari, S. P., and J. B. Warner. 2006. Econometrics of event studies. InHandbook of corporate finance: Empirical corporate finance, ed. B. E.Eckbo (Vol. 1, pp. 3–36). Amsterdam: Elsevier.

Krishnan, V., and K. T. Ulrich. 2001. Product development decisions: Areview of the Literature. Management Science 47 (1): 1–21.

Lancaster, K. 1979. Variety, equity, and efficiency. New York: ColumbiaUniversity Press.

Lancaster, K. 1990. The economics of product variety: A survey. MarketingScience 9 (Summer): 189–206.

Lane, P. J., and M. H. Lubatkin. 1998. Relative absorptive capacity andinterorganizational learning. Strategic Management Journal 19 (5):461–77.

Lee, M., and D. Na. 1994. Determinants of technical success in productdevelopment when innovative radicalness is considered. Journal ofProduct Innovation Management 11: 62–68.

Lehmann, D. R., and D. J. Reibstein. 2006. Marketing metrics and financialperformance. Cambridge, MA: Marketing Science Institute.

Lengnick-Hall, C. A. 1992. Innovation and competitive advantage: Whatwe know and what we need to learn. Journal of Management 18 (2):399–429.

Lichtenthaler, U. 2008. Open innovation in practice: An analysis of strate-gic approaches to technology transactions. IEEE Transactions on Engi-neering Management 55 (1): 148–57.

Lin, B.-W., C.-J. Chen, and H.-L. Wu. 2006. Patent portfolio diversity,technology strategy, and firm value. IEEE Transactions on EngineeringManagement 53 (1): 17–26.

Makri, M., M. A. Hitt, and P. J. Lane. 2010. Complementary technologies,knowledge relatedness, and invention outcomes in high technologymergers and acquisitions. Strategic Management Journal 31: 602–28.

March, J. G. 1991. Exploration and exploitation in organizational learning.Organization Science 2: 71–87.

Markowitz, H. M. 1959. Portfolio selection: Efficient diversification ofinvestment. New York: Wiley.

Miller, D. 1990. The Icarus paradox: How exceptional companies bringabout their own downfall. New York: Harper-Collins.

Mishra, S., D. Kim, and D. H. Lee. 1996. Factors affecting new productsuccess: Cross country comparisons. Journal of Product InnovationManagement 13: 530–50.

Moorthy, S. 1984. Market segmentation, self-selection, and product linedesign. Marketing Science 3 (4): 288–307.

More, R. A. 1982. Risk factors in accepted and rejected new industrialproducts. Industrial Marketing Management 11: 9–15.

Narin, F., E. Noma, and R. Perry. 1987. Patents as indicators of corporatetechnological strength. Research Policy 16: 143–55.

Nonaka, I., H. Takeuchi, and K. Umemoto. 1996. A theory of organizationalknowledge creation. International Journal of Technology Management11 (8): 833–46.

O’Connor, G. C. 1998. Market learning and radical innovation: A cross-case comparison of eight radical innovation projects. Journal ofProduct Innovation Management 15 (2): 151–66.

O’Connor, G. C., J. Hyland, and M. P. Rice. 2004. Bringing radical andother innovations successfully to market: Bridging the transition fromR&D to operations. In The PDMA toolbook 2 for new product devel-opment, ed. P. Belliveau, A. Griffin, and S. M. Somermeyer, 33–70.Hoboken, NJ: Wiley.

OECD. 1991. The nature of innovation and the evolution of the productivesystem. In Technology and productivity—The challenges for economicpolicy, ed. C. Freeman, 303–14. Paris: OECD.

Pakes, A. 1985. On patents, R&D, and the stock market rate of return.Journal of Political Economy 93 (2): 390–409.

Pauwels, K., J. Silva-Risso, S. Srinivasan, and D. M. Hanssens. 2004. Newproducts, sales promotions, and firm value: The case of the automobileindustry. Journal of Marketing 68 (October): 142–56.

Pisano, G. P. 1990. The R&D boundaries of the firm: An empirical analysis.Administrative Science Quarterly 35: 153–76.

Porter, M. E. 1985. Competitive advantage. New York: Free Press.

Putsis, W. P., Jr. 1997. An empirical study of the effect of brand prolifera-tion on private label-national brand pricing behavior. Review of Indus-trial Organization 12: 355–71.

Quelch, J., and D. Kenny. 1994. Extend profits, not product lines. HarvardBusiness Review 20 (September–October): 153–60.

Reichheld, F. E. 2003. The one number you need to grow. Harvard Busi-ness Review December 46–54.

Rogers, E. M. 1995. Diffusion of innovation. New York: The Free Press.

Rothaermel, F. T., M. A. Hitt, and L. A. Jobe. 2006. Balancing verticalintegration and strategic outsourcing: Effects on product portfolio,product success, and firm performance. Strategic Management Journal27 (11): 1033–56.

Roussel, P. A., K. N. Saad, and T. J. Erickson. 1991. Third generation R&D:Managing the link to corporate strategy. Cambridge, MA: HarvardBusiness School Press.

Schmalensee, R. 1978. Entry deterrence in the ready-to-eat breakfast cerealindustry. Bell Journal of Economics 9 (3): 305–27.

Schoonhoven, C. B., K. M. Eisenhardt, and K. Lyman. 1990. Speedingproducts to market: Waiting time to first product introduction in newfirms. Administrative Science Quarterly 35: 177–207.

Sorescu, A. B., R. K. Chandy, and J. C. Prabhu. 2003. Sources and financialconsequences of radical innovation: Insights from pharmaceuticals.Journal of Marketing 67 (October): 82–102.

Stavins, J. 1995. Model entry and exit in a differentiated-product industry:The personal computer market. Review of Economics and Statistics 77(4): 571–84.

Sutton, J. 1998. Technology and market structure. Cambridge, MA: MITPress.

Teece, D. J. 1986. Profiting from technological innovation. Research Policy15: 285–306.

Teece, D. J., G. Pisano, and A. Shuen. 1997. Dynamic capabilities andstrategic management. Strategic Management Journal 18 (7): 509–33.

USPTO. 2005. Medical devices: 1/1977–12/2004. Technology ProfileReport. Washington, DC: USPTO.

Utterback, J. M., and W. J. Abernathy. 1975. A dynamic model of processand product innovation. Omega 33: 639–56.

von Hippel, E. 1988. The sources of innovation. New York: OxfordUniversity Press.

Wang, H., and W.-R. Chen. 2010. Is firm-specific innovation associatedwith greater value appropriation? The roles of environmental dyna-mism and technological diversity. Research Policy 39: 141–54.

Wind, J., and V. Mahajan. 1997. Issues and opportunities in new productdevelopment: An introduction to the special issue. Journal of Market-ing Research 34 (February): 1–12.

Winter, S. G. 2006. The logic of appropriability: From Schumpeter toArrow to Teece. Research Policy 35 (8): 1100–6.


AppendixTable A1. Descriptive Statistics and Correlations

MeanStandardDeviation Min Max (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

1. Leading-Mature 8.17 21.74 0 2202. Following-Mature 5.75 18.38 0 290 .7773. Leading-New 1.16 4.53 0 101 .265 .1824. Following-New 1.96 7.89 0 146 .612 .708 .4105. Introduction intensity 1.31 4.64 0 91.8 .250 .237 .935 .5556. Pioneering intensity .59 .35 0 1 .043 −.141 .088 −.119 .0057. Size 499 1,460 .1 16,617 .632 .615 .211 .572 .241 −.0368. CProducts 2.68 3.07 0 20 −.089 .053 −.119 .015 −.077 −.535 .0439. Sales 404 1,048 0 10,054 .655 .634 .238 .561 .259 −.026 .981 .013

10. Profit 247 691 −13 7,608 .724 .681 .248 .616 .274 −.033 .964 .012 .97511. Return .21 .67 −2.6 3.39 .006 .002 .013 .032 .035 −.036 −.018 .028 −.010 −.00712. Primary patents 38.9 173.7 0 2,139 .826 .633 .315 .612 .321 −.010 .675 −.024 .659 .753 −.00113. Complementary patents 35.2 138.8 0 1,521 .456 .465 .236 .377 .219 .000 .455 −.071 .501 .467 −.001 .522

Note: In the empirical analysis, the number of product portfolio is adjusted by the number of product-market segment and firm-level intensities are mean-centered. Asa result, some of high correlations do not significantly affect the estimation results.


Documents

The Impact of Product Portfolio Strategy on Financial Performance: The Roles of Product Development and Market Entry Decisions